A Comparative Study of Breast Cancer Diagnosis Using Supervised Machine Learning Techniques

Abstract

Cancer is a class of diseases, which is driven by change in cells of the body and increase beyond normal growth and control. Breast cancer is one of the frequent types of cancer. Prognosis of breast cancer recurrence is highly required to raise the survival rate of patient suffering from breast cancer. With the advancement of technology and machine learning techniques, the cancer diagnosis and detection accuracy has improved. Machine learning (ML) techniques offer various probabilistic and statistical methods that allow intelligent systems to learn from reoccurring past experiences to detect and identify patterns from a dataset. The research work presented an overview of evolve the machine learning techniques in cancer disease by applying learning algorithms on breast cancer Wisconsin data –Linear regression, Random Forest, Multi-layer Perceptron and Decision Trees (DT). The result outcome shows that Multilayer perceptron performs better than other techniques.